code: def maskrcnn_resnet50_fpn(
      *,
      weights: Optional[MaskRCNN_ResNet50_FPN_Weights] = None,
      progress: bool = True,
      num_classes: Optional[int] = None,
      weights_backbone: Optional[
          ResNet50_Weights
      ] = ResNet50_Weights.IMAGENET1K_V1,
      trainable_backbone_layers: Optional[int] = None,
      **kwargs: Any,
  ) -> MaskRCNN:
      """Mask R-CNN model with a ResNet-50-FPN backbone from the `Mask R-CNN
      <https://arxiv.org/abs/1703.06870>`_ paper.
  
      .. betastatus:: detection module
  
      The input to the model is expected to be a list of tensors, each of shape ``[C, H, W]``, one for each
      image, and should be in ``0-1`` range. Different images can have different sizes.
  
      The behavior of the model changes depending on if it is in training or evaluation mode.
  
      During training, the model expects both the input tensors and targets (list of dictionary),
      containing:
  
          - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with
            ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
          - labels (``Int64Tensor[N]``): the class label for each ground-truth box
          - masks (``UInt8Tensor[N, H, W]``): the segmentation binary masks for each instance
  
      The model returns a ``Dict[Tensor]`` during training, containing the classification and regression
      losses for both the RPN and the R-CNN, and the mask loss.
  
      During inference, the model requires only the input tensors, and returns the post-processed
      predictions as a ``List[Dict[Tensor]]``, one for each input image. The fields of the ``Dict`` are as
      follows, where ``N`` is the number of detected instances:
  
          - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with
            ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
          - labels (``Int64Tensor[N]``): the predicted labels for each instance
          - scores (``Tensor[N]``): the scores or each instance
          - masks (``UInt8Tensor[N, 1, H, W]``): the predicted masks for each instance, in ``0-1`` range. In order to
            obtain the final segmentation masks, the soft masks can be thresholded, generally
            with a value of 0.5 (``mask >= 0.5``)
  
      For more details on the output and on how to plot the masks, you may refer to :ref:`instance_seg_output`.
  
      Mask R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size.
  
      Example::
  
          >>> model = torchvision.models.detection.maskrcnn_resnet50_fpn(weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT)
          >>> model.eval()
          >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
          >>> predictions = model(x)
          >>>
          >>> # optionally, if you want to export the model to ONNX:
          >>> torch.onnx.export(model, x, "mask_rcnn.onnx", opset_version = 11)
  
      Args:
          weights (:class:`~torchvision.models.detection.MaskRCNN_ResNet50_FPN_Weights`, optional): The
              pretrained weights to use. See
              :class:`~torchvision.models.detection.MaskRCNN_ResNet50_FPN_Weights` below for
              more details, and possible values. By default, no pre-trained
              weights are used.
          progress (bool, optional): If True, displays a progress bar of the
              download to stderr. Default is True.
          num_classes (int, optional): number of output classes of the model (including the background)
          weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The
              pretrained weights for the backbone.
          trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from
              final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are
              trainable. If ``None`` is passed (the default) this value is set to 3.
          **kwargs: parameters passed to the ``torchvision.models.detection.mask_rcnn.MaskRCNN``
              base class. Please refer to the `source code
              <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/mask_rcnn.py>`_
              for more details about this class.
  
      .. autoclass:: torchvision.models.detection.MaskRCNN_ResNet50_FPN_Weights
          :members:
      """
      weights = MaskRCNN_ResNet50_FPN_Weights.verify(weights)
      weights_backbone = ResNet50_Weights.verify(weights_backbone)
  
      if weights is not None:
          weights_backbone = None
          num_classes = _ovewrite_value_param(
              "num_classes",
              num_classes,
              len(weights.meta["categories"]),
          )
      elif num_classes is None:
          num_classes = 91
  
      is_trained = weights is not None or weights_backbone is not None
      trainable_backbone_layers = _validate_trainable_layers(
          is_trained, trainable_backbone_layers, 5, 3
      )
      norm_layer = (
          misc_nn_ops.FrozenBatchNorm2d
          if is_trained
          else nn.BatchNorm2d
      )
  
      backbone = resnet50(
          weights=weights_backbone,
          progress=progress,
          norm_layer=norm_layer,
      )
      backbone = _resnet_fpn_extractor(
          backbone, trainable_backbone_layers
      )
      model = MaskRCNN(backbone, num_classes=num_classes, **kwargs)
  
      if weights is not None:
          model.load_state_dict(
              weights.get_state_dict(progress=progress, check_hash=True)
          )
          if weights == MaskRCNN_ResNet50_FPN_Weights.COCO_V1:
              overwrite_eps(model, 0.0)
  
      return model
  
imports: from collections import OrderedDict
  from typing import Any, Callable, Optional
  from torch import nn
  from torchvision.ops import MultiScaleRoIAlign
  from ...ops import misc
  from ...transforms._presets import ObjectDetection
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _COCO_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface
  from ..resnet import resnet50, ResNet50_Weights
  from ._utils import overwrite_eps
  from .backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers
  from .faster_rcnn import _default_anchorgen, FasterRCNN, FastRCNNConvFCHead, RPNHead
label_desc: Use image node_id 1 for calling a global function with example usage: maskrcnn_resnet50_fpn() and returns: model